Enterprise Data Analysis and Design

Lecture 1: Introduction to Database Technology

Johannes Gehrke [email protected] http://www.cs.cornell.edu/johannes

Course Goals

• Architectures of modern enterprise information systems • Understand the functionality of modern database and data mining systems • Understand where database systems and data mining fit into an enterprise information system • Learn to ask the right questions • Learn how to use several important tools • Data modeling (DeZign for Databases) • Data mining (SAS Enterprise Miner)

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DeZign for Databases

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NBA 518: Enterprise Data Design and Analysis 1 SAS Enterprise Miner

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Course Outline

• 1/26 Database Management Systems • 1/28 Enterprise Information Architectures • 2/2, 2/4, and 2/9: Data Modeling • 2/11, 2/16, 2/18, 2/23, and 2/25: Data Mining • 3/2 OLAP • 3/4 Web Services • 3/9 Future Trends

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Course Mechanics

• Temporary course homepage: http://www.cs.cornell.edu/johannes/teaching/NBA518 • Slides will be online the morning before each lecture • Readings for each class will be available online • Office hours: • Tuesdays 1:30-2:30, Upson Hall 4105B • Mondays and Wednesdays from 2:00 – start of class in Sage Hall Atrium • Always welcome to ask questions via email ([email protected]) • Ask questions after the lecture NBA 518 Spring 2004: Lecture 1 6

NBA 518: Enterprise Data Design and Analysis 2 Grading

• Five homework assignments: • Enterprise architectures (15%) • Data modeling (15%) • Data mining I: Classification (15%) • Data mining II: Clustering and Associations (15%) • A complete case study (in groups of 2-3 students, 20%)

• Class participation (20%): Quality and not quantity counts

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Introduction: About the Instructor

Johannes Gehrke is an Assistant Professor in the Department of Computer Science at Cornell University. He obtained his Ph.D. in computer science from the University of Wisconsin-Madison in 1999; his graduate studies were supported by a Fulbright fellowship and an IBM fellowship.

Johannes' research interests are in the areas of data mining, data stream processing, and distributed data management for sensor networks and peer-to-peer networks. Johannes has received a National Science Foundation Career Award, an Arthur P. Sloan Fellowship, an IBM Faculty Award, and the Cornell College of Engineering James and Mary Tien Excellence in Teaching Award. He is the author of numerous publications on data mining and database systems, and he co-authored the undergraduate textbook Database Management Systems (McGrawHill (2002), currently in its third edition), used at universities all over the world.

Johannes has served as Program Co-Chair of the 2001 ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, Tutorial Chair for the 2001 IEEE International Conference on Data Mining, Area Chair for the Twentieth International Conference on Machine Learning, co-Chair of the 2003 ACM SIGKDD Cup, and he is serving as Program co-Chair of the 2004 ACM SIGKDD Conference.

Johannes has given courses and tutorials on data mining and data stream processing at international conferences and on Wall Street, and he has extensive industry experience as technical advisor.

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Introduction: Students

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NBA 518: Enterprise Data Design and Analysis 3 Goal of This Lecture

• Understand the basic functionality of a database system

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The Big Picture

WWW Site Internal User Visitor INTRANET, VPN THE WEB Internal Main Public Web Server Web Server Memory Cache Data Business Warehouse Transaction Application DBMS Server Server NBA 518 Spring 2004: Lecture 1 11

Why Database Systems?

Discuss with your neighbor: What functionality is required from database systems in the following application scenarios:

• EBay (www.ebay.com) • Barnes and Noble (www.bn.com) • General Motors (www.gm.com) • The Protein Data Bank (http://www.rcsb.org/pdb) • Sprint (www.sprint.com) • Your cell phone NBA 518 Spring 2004: Lecture 1 12

NBA 518: Enterprise Data Design and Analysis 4 Why Store Data in a DBMS?

• Benefits • Transactions (concurrent data access, recovery from system crashes) • High-level abstractions for data access, manipulation, and administration • Data integrity and security • Performance and scalability

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A Digress – What Is a Transaction?

The execution of a program that performs a function by accessing a database. Examples: • Reserve an airline seat. Buy an airline ticket. • Withdraw money from an ATM. • Verify a credit card sale. • Order an item from an Internet retailer. • Download a video clip and pay for it. • Play a bid at an on-line auction.

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Transactions

• A transaction is an atomic sequence of actions • Each transaction must leave the system in a consistent state (if system is consistent when the transaction starts). • The ACID Properties: • Atomicity • Consistency • Isolation • Durability

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NBA 518: Enterprise Data Design and Analysis 5 Example Transaction: Online Store

Your purchase transaction: • Atomicity: Either the complete purchase happens, or nothing • Consistency: The inventory and internal accounts are updated correctly • Isolation: It does not matter whether other customers are also currently making a purchase • Durability: Once you have received the order confirmation number, your order information is permanent, even if the site crashes

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Transactions (Contd.)

A transaction will commit after completing all its actions, or it could abort (or be aborted by the DBMS) after executing some actions.

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Example Transaction: ATM

You withdraw money from the ATM machine • Atomicity • Consistency • Isolation • Durability

Commit versus Abort? What are reasons for commit or abort?

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NBA 518: Enterprise Data Design and Analysis 6 Transactions: Examples

Give examples of transactions in the following applications. Which of the ACID properties are needed?

What Makes Transaction Processing Hard

• Reliability - system should rarely fail • Availability - system must be up all the time • Response time - within 1-2 seconds • Throughput - thousands of transactions/second • Scalability - start small, ramp up to Internet-scale • Security – for confidentiality and high finance • Configurability - for above requirements + low cost • Atomicity - no partial results • Durability - a transaction is a legal contract • Distribution - of users and data

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Reliability and Availability

• Reliability - system should rarely fail • Availability - system must be up all the time

Downtime Availability 1 hour/day 95.8% 1 hour/week 99.41% 1 hour/month 99.86% 1 hour/year 99.9886% 1 minute/day 99.9988% 1 hour/20years 99.99942% 1 minute/week 99.99983%

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NBA 518: Enterprise Data Design and Analysis 7 Performance

• Response time - within 1-2 seconds • Throughput - thousands of transactions/second • Scalability - start small, ramp up to Internet- scale

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What Makes TP Important?

• It is at the core of electronic commerce • Most medium-to-large businesses use TP for their production systems. The business can’t operate without it. • It is a huge slice of the computer system market — over $50B/year. Probably the single largest application of computers.

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TP System Infrastructure

• User’s viewpoint • Enter a request from a browser or other display device • The system performs some application-specific work, which includes database accesses • Receive a reply (usually, but not always) • The TP system ensures that each transaction • is an independent unit of work • executes exactly once, and • produces permanent results. • TP system makes it easy to program transactions • TP system has tools to make it easy to manage

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NBA 518: Enterprise Data Design and Analysis 8 TP System Infrastructure

End-User

Presentation Manager Front-End (Client) requests

Workflow Control (routes requests) Back-End Transaction Program (Server)

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System Characteristics

• Typically < 100 transaction types per application • Transaction size has high variance. Typically, • 0-30 disk accesses • 10K - 1M instructions executed • 2-20 messages • A large-scale example: airline reservations • 150,000 active display devices • plus indirect access via Internet travel agents • thousands of disk drives • 3000 transactions per second, peak

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Exercise

• Question: Think of a TP System that you know of, and discuss with your neighbor the requirements above. Contrast this with requirements of • The Cornell Electronic Directory • amazon.com NBA 518 Spring 2004: Lecture 1 27

NBA 518: Enterprise Data Design and Analysis 9 Concurrency Control for Isolation

(Start: A=$100; B=$100)

Consider two transactions: • T1: START, A=A+100, B=B-100, COMMIT • T2: START, A=1.06*A, B=1.06*B, COMMIT

The first transaction is transferring $100 from B’s account to A’s account. The second transaction is crediting both accounts with a 6% interest payment.

Database systems try to do as many operations concurrently as possible, to increase performance.

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Example (Contd.)

(Start: A=$100; B=$100)

• Consider a possible interleaving (schedule): T1: A=A+$100, B=B-$100 COMMIT T2: A=1.06*A, B=1.06*B COMMIT End result: A=$106; B=$0

• Another possible interleaving: T1: A=A+100, B=B-100 COMMIT T2: A=1.06*A, B=1.06*B COMMIT End result: A=$112; B=$6

The second interleaving is incorrect! Concurrency control of a database system makes sure that the second schedule does not happen. NBA 518 Spring 2004: Lecture 1 29

Ensuring Atomicity

• DBMS ensures atomicity (all-or-nothing property) even if the system crashes in the middle of a transaction. • Idea: Keep a log (history) of all actions carried out by the DBMS while executing : • Before a change is made to the database, the corresponding log entry is forced to a safe location. • After a crash, the effects of partially executed transactions are undone using the log.

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NBA 518: Enterprise Data Design and Analysis 10 Recovery

• A DBMS logs all elementary events on stable storage. This data is called the log. • The log contains everything that changes data: Inserts, updates, and deletes. • Reasons for logging: • Need to UNDO transactions • Recover from a systems crash

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Recovery: Example

(Simplified process) • Insert customer data into the database • Check order availability • Insert order data into the database • Write recovery data (the log) to stable storage • Return order confirmation number to the customer

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Why Store Data in a DBMS?

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NBA 518: Enterprise Data Design and Analysis 11 Data Model

• A data model is a collection of concepts for describing data. • Examples: • ER model (used for conceptual modeling) • Relational model, object-oriented model, object-relational model (actually implemented in current DBMS)

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The Relational Data Model

A relational database is a set of relations. Turing Award (Nobel Price in CS) for Codd in 1980 for his work on the relational model • Example relation: Customers(cid: integer, name: string, byear: integer, state: string)

cid name byear state 1 Jones 1960 NY 2 Smith 1974 CA 3 Smith 1950 NY

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The Relational Model: Terminology

• Relation instance and schema • Field (column) • Record or tuple (row) • Cardinality

cid name byear state 1 Jones 1960 NY 2 Smith 1974 CA 3 Smith 1950 NY

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NBA 518: Enterprise Data Design and Analysis 12 Customer Relation (Contd.)

• In your enterprise, you are more likely to have a schema similar to the following:

Customers(cid, identifier, nameType, salutation, firstName, middleNames, lastName, culturalGreetingStyle, gender, customerType, degrees, ethnicity, companyName, departmentName, jobTitle, primaryPhone, primaryFax, email, website, building, floor, mailstop, addressType, streetNumber, streetName, streetDirection, POBox, city, state, zipCode, region, country, assembledAddressBlock, currency, maritalStatus, bYear, profession)

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Product Relation

• Relation schema: Products(pid: integer, pname: string, price: float, category: string) • Relation instance: pid pname price category 1 Intel PIII-700 300.00 hardware 2 MS Office Pro 500.00 software 3 IBM DB2 5000.00 software 4 Thinkpad 600E 5000.00 hardware

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Transaction Relation

• Relation schema: • Relation instance: Transactions( tid: integer, tid tdate cid pid tdate: date, 1 1/1/2000 1 1 cid: integer, 1 1/1/2000 1 2 pid: integer) 2 1/1/2000 1 4 3 2/1/2000 2 3 3 2/1/2000 2 4

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NBA 518: Enterprise Data Design and Analysis 13 The Relational DBMS Market

Market Share Of RDBMS In 1998 (Gartner Group 4/1999)

70.0% 60.0% 50.0% 40.0% NT 30.0% Unix 20.0% 10.0% 0.0% NCR Other SQL Informix Sybase IBM DB2 IBM and Liteand Microsoft ASA/ASA Oracle 7/8

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The Relational DBMS Market (Contd.)

Best-Selling Client/Server DBMS (Computer Reseller News 8/1999)

50% 43%41% 40% 32% 30% 25% 22% Jan-Jun 98 20% 16% Jan-Jun 99

10% 6%6% 4%5% 0% Microsoft Oracle Sybase IBM Others

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The Relational DBMS Market (Contd.)

Market Share Of Database Revenues (Computer Reseller News, 5/1999)

35.0% 30.0% 25.0% 20.0% In 1997 15.0% In 1998 10.0% 5.0% 0.0% IBM Oracle Microsoft Informix Sybase Other

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NBA 518: Enterprise Data Design and Analysis 14 The Object-Oriented Data Model

• Richer data model. Goal: Bridge impedance mismatch between programming languages and the database system. • Example components of the data model: Relationships between objects directly as pointers. • Result: Can store abstract data types directly in the DBMS • Pictures • Geographic coordinates • Movies • CAD objects NBA 518 Spring 2004: Lecture 1 43

Object-Oriented DBMS

• Advantages: Engineering applications (CAD and CAM and CASE computer aided software engineering), multimedia applications. • Disadvantages: • Technology not as mature as relational DMBS • Not suitable for decision support, weak security • Vendors are much smaller companies and their financial stability is questionable.

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Object-Oriented DBMS (Contd.)

Vendors: • Gemstone (www.gemstone.com) • Objectivity (www.objy.com) • ObjectStore (www.objectstore.net) • POET (www.poet.com) • Versant (www.versant.com, merged with POET) Organizations: • OMG: Object Management Group (www.omg.org)

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NBA 518: Enterprise Data Design and Analysis 15 The OO DBMS Market

Forecast Revenues For OO Systems Software Worldwide (IDC 3/1999)

1,000.0 800.0 600.0 400.0 Million $ Million 200.0 0.0 1997 1998 1999 2000 2001 2002 Year

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Object-Relational DBMS

• Mixture between the object-oriented and the object-relational data model • Combines ease of querying with ability to store abstract data types • Conceptually, the relational model, but every field • All major relational vendors are currently extending their relational DBMS to the object-relational model

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Query Languages

We need a high-level language to describe and manipulate the data

Requirements: • Precise semantics • Easy integration into applications written in C++/Java/Visual Basic/etc. • Easy to learn • DBMS needs to be able to efficiently evaluate queries written in the language

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NBA 518: Enterprise Data Design and Analysis 16 Relational Query Languages

• The relational model supports simple, powerful querying of data. • Precise semantics for relational queries • Efficient execution of queries by the DBMS • Independent of physical storage

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SQL: Structured Query Language

• Developed by IBM (System R) in the 1970s • ANSI standard since 1986: • SQL-86 • SQL-89 (minor revision) • SQL-92 (major revision, current standard) • SQL-99 (major extensions) • More about SQL in the next lecture

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Example Query

• Example Schema: Customers( cid name byear state cid: integer, 1 Jones 1960 NY name: string, 2 Smith 1974 CA byear: integer, 3 Smith 1950 NY state: string) • Query: SELECT Customers.cid, Customers.name, cid name byear state Customers.byear, 3 Smith 1950 NY Customers.state FROM Customers WHERE Customers.cid = 3

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NBA 518: Enterprise Data Design and Analysis 17 Example Query

SELECT Customers.cid, Customers.name, cid name byear state Customers.byear, 1 Jones 1960 NY Customers.state 2 Smith 1974 CA 3 Smith 1950 NY FROM Customers

WHERE Customers.cid = 1 cid name byear state 1 Jones 1960 NY

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Why Store Data in a DBMS?

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Integrity Constraints

• Integrity Constraints (ICs): Condition that must be true for any instance of the database. • ICs are specified when schema is defined. • ICs are checked when relations are modified. • A legal instance of a relation is one that satisfies all specified ICs. • DBMS should only allow legal instances. • Example: Domain constraints.

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NBA 518: Enterprise Data Design and Analysis 18 Primary Key Constraints

• A set of fields is a superkey for a relation if no two distinct tuples can have same values in all key fields. • A set of fields is a key if the set is a superkey, and none of its subsets is a superkey. • Example: • {cid, name} is a superkey for Customers • {cid} is a key for Customers • Where do primary key constraints come from?

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Primary Key Constraints (Contd.)

• Can there be more than one key for a relation? • What is the maximum number of superkeys for a relation? • What is the primary key of the Products relation? How about the Transactions relation?

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Foreign Keys, Referential Integrity

• Foreign key : Set of fields in one relation that is refers to a unique tuple in another relation. (The foreign key must be a superkey of the second relation.) • Example: The field cid in the Transactions relation is a foreign key referring to Customers. • If all foreign key constraints are enforced, we say that referential integrity is achieved. • No dangling references. • Compare to links in HTML.

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NBA 518: Enterprise Data Design and Analysis 19 Foreign Keys: Example

• The pid field of the Transactions relation refers to the cid field of the Customer relation.

tid tdate cid pid 1 1/1/2000 1 1 cid name byear state 1 1/1/2000 1 2 1 Jones 1960 NY 2 1/1/2000 1 4 2 Smith 1974 CA 3 Smith 1950 NY 3 2/1/2000 2 3

3 2/1/2000 2 4

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Enforcing Referential Integrity

• What should be done if a Transaction tuple with a non-existent Customer id is inserted? (Reject it!) • What should be done if a Customer tuple is deleted? • Also delete all Transaction tuples that refer to it. • Disallow deletion of a Customer tuple that has associated Transactions. • Set cid in Transactions tuples to a default or special cid. • SQL supports all three choices

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Where do ICs Come From?

• ICs are based upon the semantics of the real- world enterprise that is being described in the database relations. • We can check a database instance to see if an IC is violated, but we can NEVER infer that an IC is true by looking at an instance. • An IC is a statement about all possible instances! • From example, we know state cannot be a key, but the assertion that cid is a key is given to us. • Key and foreign key ICs are very common; a DBMS supports more general ICs.

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NBA 518: Enterprise Data Design and Analysis 20 Security

•Secrecy:Users should not be able to see things they are not supposed to. • E.g., A student can’t see other students’ grades. •Integrity:Users should not be able to modify things they are not supposed to. • E.g., Only instructors can assign grades. • Availability: Users should be able to see and modify things they are allowed to.

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Discretionary Access Control

• Based on the concept of access rights or privileges for objects (tables and views), and mechanisms for giving users privileges (and revoking privileges). • Creator of data automatically gets all privileges on it. • DMBS keeps track of who subsequently gains and loses privileges, and ensures that only requests from users who have the necessary privileges (at the time the request is issued) are allowed. • Users can grant and revoke privileges

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Role-Based Authorization

• In SQL-92, privileges are actually assigned to authorization ids, which can denote a single user or a group of users. • In SQL:1999 (and in many current systems), privileges are assigned to roles. • Roles can then be granted to users and to other roles. • Reflects how real organizations work. • Illustrates how standards often catch up with “de facto” standards embodied in popular systems.

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NBA 518: Enterprise Data Design and Analysis 21 Security Is Important

Financial estimate of database losses, by activity in 1998 (ENT/CSI/FBI Computer Crime Survey, 5/1999)

Activity Loss in million $ Theft of Proprietary Information 28.51 System Penetration by Outsiders 13.39 Financial Fraud 11.24 Unauthorized Insider Access 50.57 Laptop Theft 0.16 Total 103.87

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Why Store Data in a DBMS?

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DBMS and Performance

• Efficient implementation of all database operations • Indexes: Auxiliary structures that allow fast access to the portion of data that a query is about • Smart buffer management • Query optimization: Finds the best way to execute a query • Automatic high-performance concurrent query execution, query parallelization

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NBA 518: Enterprise Data Design and Analysis 22 Summary Of DBMS Benefits

• Transactions • ACID properties, concurrency control, recovery • High-level abstractions for data access • Data models • Data integrity and security • Key constraints, foreign key constraints, access control • Performance and scalability • Parallel DBMS, distributed DBMS, performance tuning

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The Big Picture (Revisited)

Next Lecture

• More about the components outside the database system: • Three-tier architectures • Application servers • Middleware

• Readings for next lecture will be online tomorrow morning, slides will be online Wednesday morning

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NBA 518: Enterprise Data Design and Analysis 23